Minimizing total weighted tardiness on a batch-processing machine with non-agreeable release times and due dates

Mathirajan, M.; Bhargav, Vanamala Narasimha; Ramachandran, V.

doi:10.1007/s00170-009-2342-y

Cited by 27 publications

(19 citation statements)

References 27 publications

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“…Chou and Wang (2008) proposed a hybrid GA to minimise TWT for a single batch-processing machine problem. Mathirajan et al (2010) empirically addressed the computational complexity of the problem through an integer linear programming (ILP) model, and proposed four simple constructive heuristic algorithms and an SA to obtain solutions efficiently. Both studies have used meta-heuristic algorithms (GA and SA) to obtain robust and high-quality solutions within a reasonable computation CPU time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Proposed algorithms are examined on a benchmark problem set, which can be accessed at http://scheduling.iem.cyu.edu.tw/scheduling/problems.html. Another test problem set is generated according to the experimental design provided by Mathirajan et al (2010) to evaluate the performance of the proposed algorithms and to discuss the influence of the …”

Section: Literature Reviewmentioning

confidence: 99%

“…Both studies have applied mathematical programming models to solve smallscale problems. For large-scale problems, Chou and Wang (2008) proposed a genetic algorithm (GA) to obtain near optimal solutions, whereas Mathirajan et al (2010) proposed simulated annealing (SA). Both are meta-heuristic algorithms that need to find appropriate parameters in advance.…”

Section: Introductionmentioning

confidence: 99%

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Solving single batch-processing machine problems using an iterated heuristic

Wang

2011

International Journal of Production Research

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This article considers the problem of minimising total weighted tardiness on a batch-processing machine with compatible product families, job release dates and non-identical job sizes. A batch-processing machine can process several jobs as long as the total size of jobs in the batch does not exceed the machine's capacity. All jobs in the batch are started and completed at the same time. Batch processing time is equal to the longest job processing time among jobs in the batch. This article proposes a two-phase heuristic, in which a population-based reasoning approach is developed to determine the sequence of jobs based on their intensity values at positions in the first phase, and a dynamic programming algorithm to divide the ordered jobs into batches sequentially. An iterated heuristic is also proposed to improve solution quality further. Computational results show that high-quality solutions can be obtained by the proposed heuristic algorithms in a very short time.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solving single batch-processing machine problems using an iterated heuristic

Wang

2011

International Journal of Production Research

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“…The semiconductor burn-in scheduling problem was first introduced by Lee et al (2006) and then studied by Mathirajan et al (2010). The purpose of burn-in operation is to test the integrated circuit (IC) chips.…”

Section: Burn-in Operationmentioning

confidence: 99%

Lot Processing in Hybrid Flow Shop Scheduling Problem

Burtseva¹,

Romero²,

Ramirez³

et al. 2012

Production Scheduling

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“…Perez et al [9] and Ren et al [10] studied the single-batch processing problem with the objective of minimum total weighted tardiness and minimum weighted number of tardy jobs, respectively, and developed several heuristics and tested their performances. Chiang et al [11] and Mathirajan et al [12] studied singlebatch processing problem with the objective of minimum total weighted tardiness and proposed metaheuristic algorithms, including memetic algorithm and SA, respectively, to solve that problem.…”

Section: Introductionmentioning

confidence: 99%

The openshop batch processing problem with non-identical processing times, using simulated annealing and genetic algorithms approaches

Khormali¹,

Mirzazadeh²,

Faez³

2011

Int J Adv Manuf Technol

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This paper addresses batch processing problem on the multimachine openshop. In the batch processing problem, jobs are categorized into batches and the batches are processed by machines. The processing time of each batch equals the maximum processing time of the batch jobs. The objective is to minimize the makespan. We assume non-identical processing time jobs. We used metaheuristic algorithms, including simulated annealing and genetic algorithms to solve the problem. Several numerical experiments show that for the small size problems, the proposed methods generate the solutions by no deviation or a little deviation from the global optimum. Furthermore, for the large-size problems, the proposed methods generate better solutions in a few times as compared to the commercial optimization software Lingo 8.

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Minimizing total weighted tardiness on a batch-processing machine with non-agreeable release times and due dates

Cited by 27 publications

References 27 publications

Solving single batch-processing machine problems using an iterated heuristic

Solving single batch-processing machine problems using an iterated heuristic

Lot Processing in Hybrid Flow Shop Scheduling Problem

The openshop batch processing problem with non-identical processing times, using simulated annealing and genetic algorithms approaches

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